Discover gists
| ProjectName="ExampleApp" | |
| rm -rf "$ProjectName.xcodeproj/xcuserdata" | |
| rm -rf "$ProjectName.xcodeproj/project.xcworkspace" | |
| rm -rf "$ProjectName.xcodeproj/project.xcworkspace/xcuserdata" |
| /* | |
| * Copyright (c) 2026 OkHttp Authors | |
| * | |
| * Licensed under the Apache License, Version 2.0 (the "License"); | |
| * you may not use this file except in compliance with the License. | |
| * You may obtain a copy of the License at | |
| * | |
| * http://www.apache.org/licenses/LICENSE-2.0 | |
| * | |
| * Unless required by applicable law or agreed to in writing, software |
It turns out that MacOS Tahoe can generate and use secure-enclave backed SSH keys! This replaces projects like https://github.com/maxgoedjen/secretive
There is a shared library /usr/lib/ssh-keychain.dylib that traditionally has been used to add smartcard support
to ssh by implementing PKCS11Provider interface. However since recently it also implements SecurityKeyProivder
which supports loading keys directly from the secure enclave! SecurityKeyProvider is what is normally used to talk to FIDO2 devices (e.g. libfido2 can be used to talk to your Yubikey). However you can now use it to talk to your Secure Enclave instead!
| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
| !tuple |
| import random | |
| attributePointsRemaining = 330 | |
| weightAttribute = [5, 5, 5, 5, 5, 5, 7, 7] | |
| flm = "Flamecharm" | |
| ice = "Frostdraw" | |
| wnd = "Galebreathe" | |
| ltn = "Thundercall" |
| #pragma once | |
| // | |
| // Magic Ring Buffer | |
| // https://gist.github.com/mmozeiko/3b09a340f3c53e5eaed699a1aea95250 | |
| // | |
| // Sets up memory mapping so the same memory block follows itself in virtual address space: | |
| // | |
| // [abcd...xyz][abc...xyz] | |
| // |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.